Overview

Brought to you by YData

Dataset statistics

Number of variables34
Number of observations3313522
Missing cells16496269
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory859.5 MiB
Average record size in memory272.0 B

Variable types

DateTime3
Categorical8
Numeric15
Text8

Alerts

AIRLINE has constant value "Southwest Airlines Co." Constant
AIRLINE_DOT has constant value "Southwest Airlines Co.: WN" Constant
AIRLINE_CODE has constant value "WN" Constant
DOT_CODE has constant value "19393" Constant
CANCELLED is highly imbalanced (83.8%) Imbalance
DIVERTED is highly imbalanced (97.7%) Imbalance
DEP_DELAY has 77821 (2.3%) missing values Missing
TAXI_OUT has 78301 (2.4%) missing values Missing
TAXI_IN has 79338 (2.4%) missing values Missing
ARR_DELAY has 85785 (2.6%) missing values Missing
CANCELLATION_CODE has 3235034 (97.6%) missing values Missing
ELAPSED_TIME has 85785 (2.6%) missing values Missing
AIR_TIME has 85785 (2.6%) missing values Missing
DELAY_DUE_CARRIER has 2553684 (77.1%) missing values Missing
DELAY_DUE_WEATHER has 2553684 (77.1%) missing values Missing
DELAY_DUE_NAS has 2553684 (77.1%) missing values Missing
DELAY_DUE_SECURITY has 2553684 (77.1%) missing values Missing
DELAY_DUE_LATE_AIRCRAFT has 2553684 (77.1%) missing values Missing
DELAY_DUE_SECURITY is highly skewed (γ1 = 49.64198616) Skewed
DEP_DELAY has 215759 (6.5%) zeros Zeros
ARR_DELAY has 66616 (2.0%) zeros Zeros
DELAY_DUE_CARRIER has 243307 (7.3%) zeros Zeros
DELAY_DUE_WEATHER has 744337 (22.5%) zeros Zeros
DELAY_DUE_NAS has 470725 (14.2%) zeros Zeros
DELAY_DUE_SECURITY has 756232 (22.8%) zeros Zeros
DELAY_DUE_LATE_AIRCRAFT has 232743 (7.0%) zeros Zeros

Reproduction

Analysis started2024-10-18 17:44:06.424090
Analysis finished2024-10-18 17:46:38.645476
Duration2 minutes and 32.22 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct973
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
Minimum2021-01-01 00:00:00
Maximum2023-08-31 00:00:00
2024-10-18T20:46:38.733070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:38.857134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AIRLINE
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
Southwest Airlines Co.
3313522 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters72897484
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouthwest Airlines Co.
2nd rowSouthwest Airlines Co.
3rd rowSouthwest Airlines Co.
4th rowSouthwest Airlines Co.
5th rowSouthwest Airlines Co.

Common Values

ValueCountFrequency (%)
Southwest Airlines Co. 3313522
100.0%

Length

2024-10-18T20:46:38.971205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T20:46:39.064666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
southwest 3313522
33.3%
airlines 3313522
33.3%
co 3313522
33.3%

Most occurring characters

ValueCountFrequency (%)
o 6627044
 
9.1%
t 6627044
 
9.1%
e 6627044
 
9.1%
s 6627044
 
9.1%
6627044
 
9.1%
i 6627044
 
9.1%
S 3313522
 
4.5%
u 3313522
 
4.5%
h 3313522
 
4.5%
w 3313522
 
4.5%
Other values (6) 19881132
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72897484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 6627044
 
9.1%
t 6627044
 
9.1%
e 6627044
 
9.1%
s 6627044
 
9.1%
6627044
 
9.1%
i 6627044
 
9.1%
S 3313522
 
4.5%
u 3313522
 
4.5%
h 3313522
 
4.5%
w 3313522
 
4.5%
Other values (6) 19881132
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72897484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 6627044
 
9.1%
t 6627044
 
9.1%
e 6627044
 
9.1%
s 6627044
 
9.1%
6627044
 
9.1%
i 6627044
 
9.1%
S 3313522
 
4.5%
u 3313522
 
4.5%
h 3313522
 
4.5%
w 3313522
 
4.5%
Other values (6) 19881132
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72897484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 6627044
 
9.1%
t 6627044
 
9.1%
e 6627044
 
9.1%
s 6627044
 
9.1%
6627044
 
9.1%
i 6627044
 
9.1%
S 3313522
 
4.5%
u 3313522
 
4.5%
h 3313522
 
4.5%
w 3313522
 
4.5%
Other values (6) 19881132
27.3%

AIRLINE_DOT
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
Southwest Airlines Co.: WN
3313522 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters86151572
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouthwest Airlines Co.: WN
2nd rowSouthwest Airlines Co.: WN
3rd rowSouthwest Airlines Co.: WN
4th rowSouthwest Airlines Co.: WN
5th rowSouthwest Airlines Co.: WN

Common Values

ValueCountFrequency (%)
Southwest Airlines Co.: WN 3313522
100.0%

Length

2024-10-18T20:46:39.160667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T20:46:39.244823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
southwest 3313522
25.0%
airlines 3313522
25.0%
co 3313522
25.0%
wn 3313522
25.0%

Most occurring characters

ValueCountFrequency (%)
9940566
 
11.5%
t 6627044
 
7.7%
e 6627044
 
7.7%
s 6627044
 
7.7%
o 6627044
 
7.7%
i 6627044
 
7.7%
S 3313522
 
3.8%
n 3313522
 
3.8%
W 3313522
 
3.8%
: 3313522
 
3.8%
Other values (9) 29821698
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86151572
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9940566
 
11.5%
t 6627044
 
7.7%
e 6627044
 
7.7%
s 6627044
 
7.7%
o 6627044
 
7.7%
i 6627044
 
7.7%
S 3313522
 
3.8%
n 3313522
 
3.8%
W 3313522
 
3.8%
: 3313522
 
3.8%
Other values (9) 29821698
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86151572
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9940566
 
11.5%
t 6627044
 
7.7%
e 6627044
 
7.7%
s 6627044
 
7.7%
o 6627044
 
7.7%
i 6627044
 
7.7%
S 3313522
 
3.8%
n 3313522
 
3.8%
W 3313522
 
3.8%
: 3313522
 
3.8%
Other values (9) 29821698
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86151572
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9940566
 
11.5%
t 6627044
 
7.7%
e 6627044
 
7.7%
s 6627044
 
7.7%
o 6627044
 
7.7%
i 6627044
 
7.7%
S 3313522
 
3.8%
n 3313522
 
3.8%
W 3313522
 
3.8%
: 3313522
 
3.8%
Other values (9) 29821698
34.6%

AIRLINE_CODE
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
WN
3313522 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6627044
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWN
2nd rowWN
3rd rowWN
4th rowWN
5th rowWN

Common Values

ValueCountFrequency (%)
WN 3313522
100.0%

Length

2024-10-18T20:46:39.340904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T20:46:39.424938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
wn 3313522
100.0%

Most occurring characters

ValueCountFrequency (%)
W 3313522
50.0%
N 3313522
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6627044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 3313522
50.0%
N 3313522
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6627044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 3313522
50.0%
N 3313522
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6627044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 3313522
50.0%
N 3313522
50.0%

DOT_CODE
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
19393
3313522 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters16567610
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19393
2nd row19393
3rd row19393
4th row19393
5th row19393

Common Values

ValueCountFrequency (%)
19393 3313522
100.0%

Length

2024-10-18T20:46:39.515049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T20:46:39.605247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
19393 3313522
100.0%

Most occurring characters

ValueCountFrequency (%)
9 6627044
40.0%
3 6627044
40.0%
1 3313522
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 6627044
40.0%
3 6627044
40.0%
1 3313522
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 6627044
40.0%
3 6627044
40.0%
1 3313522
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 6627044
40.0%
3 6627044
40.0%
1 3313522
20.0%

FL_NUMBER
Real number (ℝ)

Distinct6812
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2060.522
Minimum1
Maximum6999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:39.707820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile215
Q1945
median1869
Q32879
95-th percentile4884
Maximum6999
Range6998
Interquartile range (IQR)1934

Descriptive statistics

Standard deviation1409.6242
Coefficient of variation (CV)0.68411022
Kurtosis0.25919642
Mean2060.522
Median Absolute Deviation (MAD)962
Skewness0.79923221
Sum6.827585 × 109
Variance1987040.3
MonotonicityNot monotonic
2024-10-18T20:46:39.846832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 2131
 
0.1%
311 1973
 
0.1%
694 1858
 
0.1%
1640 1768
 
0.1%
110 1701
 
0.1%
351 1681
 
0.1%
953 1677
 
0.1%
312 1646
 
< 0.1%
44 1642
 
< 0.1%
255 1639
 
< 0.1%
Other values (6802) 3295806
99.5%
ValueCountFrequency (%)
1 882
< 0.1%
2 743
< 0.1%
3 780
< 0.1%
4 1252
< 0.1%
5 835
< 0.1%
6 1038
< 0.1%
7 909
< 0.1%
8 440
 
< 0.1%
9 536
 
< 0.1%
10 1362
< 0.1%
ValueCountFrequency (%)
6999 9
< 0.1%
6998 1
 
< 0.1%
6997 2
 
< 0.1%
6996 1
 
< 0.1%
6995 3
 
< 0.1%
6994 1
 
< 0.1%
6993 2
 
< 0.1%
6992 9
< 0.1%
6991 2
 
< 0.1%
6990 1
 
< 0.1%

ORIGIN
Text

Distinct107
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:40.026993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9940566
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBWI
2nd rowATL
3rd rowATL
4th rowATL
5th rowATL
ValueCountFrequency (%)
den 220838
 
6.7%
las 185768
 
5.6%
mdw 183490
 
5.5%
dal 166208
 
5.0%
bwi 164385
 
5.0%
phx 153079
 
4.6%
hou 122857
 
3.7%
bna 113040
 
3.4%
mco 100394
 
3.0%
oak 90811
 
2.7%
Other values (97) 1812652
54.7%
2024-10-18T20:46:40.309960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1229398
 
12.4%
S 922880
 
9.3%
L 894710
 
9.0%
D 740601
 
7.5%
M 603965
 
6.1%
N 575518
 
5.8%
O 513100
 
5.2%
B 468334
 
4.7%
H 400059
 
4.0%
C 395880
 
4.0%
Other values (16) 3196121
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1229398
 
12.4%
S 922880
 
9.3%
L 894710
 
9.0%
D 740601
 
7.5%
M 603965
 
6.1%
N 575518
 
5.8%
O 513100
 
5.2%
B 468334
 
4.7%
H 400059
 
4.0%
C 395880
 
4.0%
Other values (16) 3196121
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1229398
 
12.4%
S 922880
 
9.3%
L 894710
 
9.0%
D 740601
 
7.5%
M 603965
 
6.1%
N 575518
 
5.8%
O 513100
 
5.2%
B 468334
 
4.7%
H 400059
 
4.0%
C 395880
 
4.0%
Other values (16) 3196121
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1229398
 
12.4%
S 922880
 
9.3%
L 894710
 
9.0%
D 740601
 
7.5%
M 603965
 
6.1%
N 575518
 
5.8%
O 513100
 
5.2%
B 468334
 
4.7%
H 400059
 
4.0%
C 395880
 
4.0%
Other values (16) 3196121
32.2%
Distinct104
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:40.496240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length30
Median length24
Mean length12.28284
Min length8

Characters and Unicode

Total characters40699461
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBaltimore, MD
2nd rowAtlanta, GA
3rd rowAtlanta, GA
4th rowAtlanta, GA
5th rowAtlanta, GA
ValueCountFrequency (%)
ca 568536
 
7.5%
tx 462803
 
6.1%
fl 289117
 
3.8%
co 235790
 
3.1%
denver 220838
 
2.9%
san 218145
 
2.9%
il 206222
 
2.7%
chicago 206222
 
2.7%
nv 205176
 
2.7%
vegas 185768
 
2.4%
Other values (155) 4805114
63.2%
2024-10-18T20:46:40.802930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4290209
 
10.5%
a 3377275
 
8.3%
, 3313522
 
8.1%
e 2143215
 
5.3%
n 2099277
 
5.2%
o 2043071
 
5.0%
l 1696941
 
4.2%
s 1682316
 
4.1%
i 1522934
 
3.7%
A 1311883
 
3.2%
Other values (44) 17218818
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40699461
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4290209
 
10.5%
a 3377275
 
8.3%
, 3313522
 
8.1%
e 2143215
 
5.3%
n 2099277
 
5.2%
o 2043071
 
5.0%
l 1696941
 
4.2%
s 1682316
 
4.1%
i 1522934
 
3.7%
A 1311883
 
3.2%
Other values (44) 17218818
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40699461
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4290209
 
10.5%
a 3377275
 
8.3%
, 3313522
 
8.1%
e 2143215
 
5.3%
n 2099277
 
5.2%
o 2043071
 
5.0%
l 1696941
 
4.2%
s 1682316
 
4.1%
i 1522934
 
3.7%
A 1311883
 
3.2%
Other values (44) 17218818
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40699461
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4290209
 
10.5%
a 3377275
 
8.3%
, 3313522
 
8.1%
e 2143215
 
5.3%
n 2099277
 
5.2%
o 2043071
 
5.0%
l 1696941
 
4.2%
s 1682316
 
4.1%
i 1522934
 
3.7%
A 1311883
 
3.2%
Other values (44) 17218818
42.3%

DEST
Text

Distinct107
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:40.977717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9940566
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBNA
2nd rowMCI
3rd rowBWI
4th rowBWI
5th rowLGA
ValueCountFrequency (%)
den 220827
 
6.7%
las 185755
 
5.6%
mdw 183492
 
5.5%
dal 166207
 
5.0%
bwi 164382
 
5.0%
phx 153072
 
4.6%
hou 122851
 
3.7%
bna 113036
 
3.4%
mco 100367
 
3.0%
oak 90808
 
2.7%
Other values (97) 1812725
54.7%
2024-10-18T20:46:41.260808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1229390
 
12.4%
S 922900
 
9.3%
L 894686
 
9.0%
D 740614
 
7.5%
M 603965
 
6.1%
N 575521
 
5.8%
O 513083
 
5.2%
B 468338
 
4.7%
H 400056
 
4.0%
C 395871
 
4.0%
Other values (16) 3196142
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1229390
 
12.4%
S 922900
 
9.3%
L 894686
 
9.0%
D 740614
 
7.5%
M 603965
 
6.1%
N 575521
 
5.8%
O 513083
 
5.2%
B 468338
 
4.7%
H 400056
 
4.0%
C 395871
 
4.0%
Other values (16) 3196142
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1229390
 
12.4%
S 922900
 
9.3%
L 894686
 
9.0%
D 740614
 
7.5%
M 603965
 
6.1%
N 575521
 
5.8%
O 513083
 
5.2%
B 468338
 
4.7%
H 400056
 
4.0%
C 395871
 
4.0%
Other values (16) 3196142
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1229390
 
12.4%
S 922900
 
9.3%
L 894686
 
9.0%
D 740614
 
7.5%
M 603965
 
6.1%
N 575521
 
5.8%
O 513083
 
5.2%
B 468338
 
4.7%
H 400056
 
4.0%
C 395871
 
4.0%
Other values (16) 3196142
32.2%
Distinct104
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:41.453091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length30
Median length24
Mean length12.282894
Min length8

Characters and Unicode

Total characters40699640
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNashville, TN
2nd rowKansas City, MO
3rd rowBaltimore, MD
4th rowBaltimore, MD
5th rowNew York, NY
ValueCountFrequency (%)
ca 568554
 
7.5%
tx 462808
 
6.1%
fl 289081
 
3.8%
co 235783
 
3.1%
denver 220827
 
2.9%
san 218157
 
2.9%
il 206229
 
2.7%
chicago 206229
 
2.7%
nv 205165
 
2.7%
vegas 185755
 
2.4%
Other values (155) 4805156
63.2%
2024-10-18T20:46:41.759506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4290222
 
10.5%
a 3377263
 
8.3%
, 3313522
 
8.1%
e 2143224
 
5.3%
n 2099314
 
5.2%
o 2043103
 
5.0%
l 1696931
 
4.2%
s 1682332
 
4.1%
i 1522972
 
3.7%
A 1311900
 
3.2%
Other values (44) 17218857
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40699640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4290222
 
10.5%
a 3377263
 
8.3%
, 3313522
 
8.1%
e 2143224
 
5.3%
n 2099314
 
5.2%
o 2043103
 
5.0%
l 1696931
 
4.2%
s 1682332
 
4.1%
i 1522972
 
3.7%
A 1311900
 
3.2%
Other values (44) 17218857
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40699640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4290222
 
10.5%
a 3377263
 
8.3%
, 3313522
 
8.1%
e 2143224
 
5.3%
n 2099314
 
5.2%
o 2043103
 
5.0%
l 1696931
 
4.2%
s 1682332
 
4.1%
i 1522972
 
3.7%
A 1311900
 
3.2%
Other values (44) 17218857
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40699640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4290222
 
10.5%
a 3377263
 
8.3%
, 3313522
 
8.1%
e 2143224
 
5.3%
n 2099314
 
5.2%
o 2043103
 
5.0%
l 1696931
 
4.2%
s 1682332
 
4.1%
i 1522972
 
3.7%
A 1311900
 
3.2%
Other values (44) 17218857
42.3%
Distinct226
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
Minimum2024-10-18 05:00:00
Maximum2024-10-18 23:55:00
2024-10-18T20:46:41.891651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:42.017790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1367
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:42.240187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters16567610
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st row12:53
2nd row16:41
3rd row11:58
4th row17:29
5th row20:22
ValueCountFrequency (%)
0:01 77821
 
2.3%
06:00 7753
 
0.2%
05:59 7361
 
0.2%
05:58 6568
 
0.2%
05:57 6311
 
0.2%
05:56 5523
 
0.2%
07:00 5486
 
0.2%
05:55 5324
 
0.2%
06:10 5279
 
0.2%
06:55 5271
 
0.2%
Other values (1357) 3180825
96.0%
2024-10-18T20:46:42.570654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 3313522
20.0%
1 3098589
18.7%
0 2406481
14.5%
2 1531188
9.2%
5 1257575
 
7.6%
3 1042379
 
6.3%
4 1018773
 
6.1%
6 732757
 
4.4%
8 713664
 
4.3%
9 705946
 
4.3%
Other values (2) 746736
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3098589
18.7%
0 2406481
14.5%
2 1531188
9.2%
5 1257575
 
7.6%
3 1042379
 
6.3%
4 1018773
 
6.1%
6 732757
 
4.4%
8 713664
 
4.3%
9 705946
 
4.3%
Other values (2) 746736
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3098589
18.7%
0 2406481
14.5%
2 1531188
9.2%
5 1257575
 
7.6%
3 1042379
 
6.3%
4 1018773
 
6.1%
6 732757
 
4.4%
8 713664
 
4.3%
9 705946
 
4.3%
Other values (2) 746736
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3098589
18.7%
0 2406481
14.5%
2 1531188
9.2%
5 1257575
 
7.6%
3 1042379
 
6.3%
4 1018773
 
6.1%
6 732757
 
4.4%
8 713664
 
4.3%
9 705946
 
4.3%
Other values (2) 746736
 
4.5%

DEP_DELAY
Real number (ℝ)

Missing  Zeros 

Distinct608
Distinct (%)< 0.1%
Missing77821
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean13.725938
Minimum-52
Maximum975
Zeros215759
Zeros (%)6.5%
Negative1251237
Negative (%)37.8%
Memory size50.6 MiB
2024-10-18T20:46:42.720768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-52
5-th percentile-6
Q1-3
median2
Q317
95-th percentile70
Maximum975
Range1027
Interquartile range (IQR)20

Descriptive statistics

Standard deviation32.522796
Coefficient of variation (CV)2.3694407
Kurtosis28.696176
Mean13.725938
Median Absolute Deviation (MAD)6
Skewness4.2815144
Sum44413031
Variance1057.7322
MonotonicityNot monotonic
2024-10-18T20:46:42.853049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 215759
 
6.5%
-1 215140
 
6.5%
-2 208355
 
6.3%
-3 201246
 
6.1%
-4 185233
 
5.6%
-5 182883
 
5.5%
1 115130
 
3.5%
-6 110519
 
3.3%
2 93100
 
2.8%
3 81586
 
2.5%
Other values (598) 1626750
49.1%
(Missing) 77821
 
2.3%
ValueCountFrequency (%)
-52 1
 
< 0.1%
-38 1
 
< 0.1%
-31 1
 
< 0.1%
-30 1
 
< 0.1%
-29 2
 
< 0.1%
-28 1
 
< 0.1%
-27 1
 
< 0.1%
-26 2
 
< 0.1%
-25 5
< 0.1%
-24 2
 
< 0.1%
ValueCountFrequency (%)
975 1
< 0.1%
775 1
< 0.1%
747 1
< 0.1%
726 1
< 0.1%
721 1
< 0.1%
705 1
< 0.1%
702 1
< 0.1%
681 1
< 0.1%
669 1
< 0.1%
648 1
< 0.1%

TAXI_OUT
Real number (ℝ)

Missing 

Distinct172
Distinct (%)< 0.1%
Missing78301
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean12.563544
Minimum1
Maximum174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:42.979187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q19
median11
Q314
95-th percentile23
Maximum174
Range173
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.3453344
Coefficient of variation (CV)0.50505926
Kurtosis47.26845
Mean12.563544
Median Absolute Deviation (MAD)2
Skewness4.7310257
Sum40645843
Variance40.263269
MonotonicityNot monotonic
2024-10-18T20:46:43.117324image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 406149
12.3%
9 393799
11.9%
11 367951
11.1%
8 318094
9.6%
12 305346
9.2%
13 241126
7.3%
7 192917
 
5.8%
14 186709
 
5.6%
15 143818
 
4.3%
16 110283
 
3.3%
Other values (162) 569029
17.2%
ValueCountFrequency (%)
1 99
 
< 0.1%
2 177
 
< 0.1%
3 283
 
< 0.1%
4 761
 
< 0.1%
5 12536
 
0.4%
6 74515
 
2.2%
7 192917
5.8%
8 318094
9.6%
9 393799
11.9%
10 406149
12.3%
ValueCountFrequency (%)
174 1
< 0.1%
172 2
< 0.1%
171 1
< 0.1%
170 2
< 0.1%
169 1
< 0.1%
168 1
< 0.1%
167 1
< 0.1%
166 1
< 0.1%
164 1
< 0.1%
163 1
< 0.1%
Distinct1374
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:43.339706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters16567610
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)< 0.1%

Sample

1st row13:02
2nd row16:53
3rd row12:06
4th row17:42
5th row20:36
ValueCountFrequency (%)
0:01 78301
 
2.4%
06:10 4930
 
0.1%
06:11 4911
 
0.1%
06:14 4881
 
0.1%
06:12 4857
 
0.1%
06:13 4823
 
0.1%
06:16 4782
 
0.1%
06:17 4762
 
0.1%
06:18 4753
 
0.1%
06:15 4698
 
0.1%
Other values (1364) 3191824
96.3%
2024-10-18T20:46:43.676513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 3313522
20.0%
1 3169534
19.1%
0 2320647
14.0%
2 1599738
9.7%
5 1117573
 
6.7%
3 1087766
 
6.6%
4 1028327
 
6.2%
6 740949
 
4.5%
8 712781
 
4.3%
9 704593
 
4.3%
Other values (2) 772180
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3169534
19.1%
0 2320647
14.0%
2 1599738
9.7%
5 1117573
 
6.7%
3 1087766
 
6.6%
4 1028327
 
6.2%
6 740949
 
4.5%
8 712781
 
4.3%
9 704593
 
4.3%
Other values (2) 772180
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3169534
19.1%
0 2320647
14.0%
2 1599738
9.7%
5 1117573
 
6.7%
3 1087766
 
6.6%
4 1028327
 
6.2%
6 740949
 
4.5%
8 712781
 
4.3%
9 704593
 
4.3%
Other values (2) 772180
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3169534
19.1%
0 2320647
14.0%
2 1599738
9.7%
5 1117573
 
6.7%
3 1087766
 
6.6%
4 1028327
 
6.2%
6 740949
 
4.5%
8 712781
 
4.3%
9 704593
 
4.3%
Other values (2) 772180
 
4.7%
Distinct1440
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:43.905125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters16567610
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row13:41
2nd row17:39
3rd row13:21
4th row19:04
5th row22:07
ValueCountFrequency (%)
0:01 79338
 
2.4%
09:38 3603
 
0.1%
09:44 3564
 
0.1%
09:36 3559
 
0.1%
18:18 3556
 
0.1%
09:56 3530
 
0.1%
09:42 3530
 
0.1%
09:45 3521
 
0.1%
09:54 3511
 
0.1%
09:47 3506
 
0.1%
Other values (1430) 3202304
96.6%
2024-10-18T20:46:44.241580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 3313522
20.0%
1 3190273
19.3%
0 2195143
13.2%
2 1881918
11.4%
3 1173911
 
7.1%
5 1054817
 
6.4%
4 1039696
 
6.3%
9 708821
 
4.3%
8 677774
 
4.1%
7 672855
 
4.1%
Other values (2) 658880
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3190273
19.3%
0 2195143
13.2%
2 1881918
11.4%
3 1173911
 
7.1%
5 1054817
 
6.4%
4 1039696
 
6.3%
9 708821
 
4.3%
8 677774
 
4.1%
7 672855
 
4.1%
Other values (2) 658880
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3190273
19.3%
0 2195143
13.2%
2 1881918
11.4%
3 1173911
 
7.1%
5 1054817
 
6.4%
4 1039696
 
6.3%
9 708821
 
4.3%
8 677774
 
4.1%
7 672855
 
4.1%
Other values (2) 658880
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3190273
19.3%
0 2195143
13.2%
2 1881918
11.4%
3 1173911
 
7.1%
5 1054817
 
6.4%
4 1039696
 
6.3%
9 708821
 
4.3%
8 677774
 
4.1%
7 672855
 
4.1%
Other values (2) 658880
 
4.0%

TAXI_IN
Real number (ℝ)

Missing 

Distinct176
Distinct (%)< 0.1%
Missing79338
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean5.8249605
Minimum1
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:44.379905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile14
Maximum186
Range185
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.3994704
Coefficient of variation (CV)0.92695399
Kurtosis74.307258
Mean5.8249605
Median Absolute Deviation (MAD)1
Skewness6.2332929
Sum18838994
Variance29.154281
MonotonicityNot monotonic
2024-10-18T20:46:44.518046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 764035
23.1%
3 630229
19.0%
5 555434
16.8%
6 352104
10.6%
2 210043
 
6.3%
7 208729
 
6.3%
8 122135
 
3.7%
9 74515
 
2.2%
10 49264
 
1.5%
11 34711
 
1.0%
Other values (166) 232985
 
7.0%
(Missing) 79338
 
2.4%
ValueCountFrequency (%)
1 17277
 
0.5%
2 210043
 
6.3%
3 630229
19.0%
4 764035
23.1%
5 555434
16.8%
6 352104
10.6%
7 208729
 
6.3%
8 122135
 
3.7%
9 74515
 
2.2%
10 49264
 
1.5%
ValueCountFrequency (%)
186 1
 
< 0.1%
184 1
 
< 0.1%
181 1
 
< 0.1%
180 1
 
< 0.1%
177 1
 
< 0.1%
176 4
< 0.1%
175 6
< 0.1%
172 1
 
< 0.1%
171 1
 
< 0.1%
170 1
 
< 0.1%
Distinct239
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
Minimum2024-10-18 00:05:00
Maximum2024-10-18 23:55:00
2024-10-18T20:46:44.650227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:44.776470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1440
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:44.980764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters16567610
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row13:47
2nd row17:48
3rd row13:31
4th row19:28
5th row22:14
ValueCountFrequency (%)
0:01 79323
 
2.4%
18:01 3561
 
0.1%
09:45 3534
 
0.1%
18:40 3532
 
0.1%
09:50 3530
 
0.1%
09:46 3524
 
0.1%
09:43 3517
 
0.1%
09:44 3516
 
0.1%
09:40 3515
 
0.1%
09:38 3514
 
0.1%
Other values (1430) 3202456
96.6%
2024-10-18T20:46:45.587595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 3313522
20.0%
1 3194427
19.3%
0 2196383
13.3%
2 1899189
11.5%
3 1177806
 
7.1%
5 1051251
 
6.3%
4 1037880
 
6.3%
9 705139
 
4.3%
8 676822
 
4.1%
7 668145
 
4.0%
Other values (2) 647046
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3194427
19.3%
0 2196383
13.3%
2 1899189
11.5%
3 1177806
 
7.1%
5 1051251
 
6.3%
4 1037880
 
6.3%
9 705139
 
4.3%
8 676822
 
4.1%
7 668145
 
4.0%
Other values (2) 647046
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3194427
19.3%
0 2196383
13.3%
2 1899189
11.5%
3 1177806
 
7.1%
5 1051251
 
6.3%
4 1037880
 
6.3%
9 705139
 
4.3%
8 676822
 
4.1%
7 668145
 
4.0%
Other values (2) 647046
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16567610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 3313522
20.0%
1 3194427
19.3%
0 2196383
13.3%
2 1899189
11.5%
3 1177806
 
7.1%
5 1051251
 
6.3%
4 1037880
 
6.3%
9 705139
 
4.3%
8 676822
 
4.1%
7 668145
 
4.0%
Other values (2) 647046
 
3.9%

ARR_DELAY
Real number (ℝ)

Missing  Zeros 

Distinct638
Distinct (%)< 0.1%
Missing85785
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean6.746238
Minimum-71
Maximum812
Zeros66616
Zeros (%)2.0%
Negative1791298
Negative (%)54.1%
Memory size50.6 MiB
2024-10-18T20:46:45.733950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-71
5-th percentile-22
Q1-12
median-3
Q313
95-th percentile68
Maximum812
Range883
Interquartile range (IQR)25

Descriptive statistics

Standard deviation34.84579
Coefficient of variation (CV)5.1652179
Kurtosis22.973001
Mean6.746238
Median Absolute Deviation (MAD)11
Skewness3.7090012
Sum21775082
Variance1214.229
MonotonicityNot monotonic
2024-10-18T20:46:45.866235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 94898
 
2.9%
-9 94426
 
2.8%
-8 94154
 
2.8%
-11 93877
 
2.8%
-7 91496
 
2.8%
-12 90985
 
2.7%
-13 88535
 
2.7%
-6 88268
 
2.7%
-5 85289
 
2.6%
-14 83572
 
2.5%
Other values (628) 2322237
70.1%
(Missing) 85785
 
2.6%
ValueCountFrequency (%)
-71 1
 
< 0.1%
-69 3
 
< 0.1%
-68 2
 
< 0.1%
-67 1
 
< 0.1%
-66 5
< 0.1%
-65 3
 
< 0.1%
-64 5
< 0.1%
-63 6
< 0.1%
-62 6
< 0.1%
-61 10
< 0.1%
ValueCountFrequency (%)
812 1
< 0.1%
810 1
< 0.1%
760 1
< 0.1%
731 1
< 0.1%
714 1
< 0.1%
697 1
< 0.1%
691 1
< 0.1%
667 1
< 0.1%
655 1
< 0.1%
652 1
< 0.1%

CANCELLED
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
0.0
3235034 
1.0
 
78488

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9940566
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3235034
97.6%
1.0 78488
 
2.4%

Length

2024-10-18T20:46:45.980381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T20:46:46.070935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3235034
97.6%
1.0 78488
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 6548556
65.9%
. 3313522
33.3%
1 78488
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6548556
65.9%
. 3313522
33.3%
1 78488
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6548556
65.9%
. 3313522
33.3%
1 78488
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6548556
65.9%
. 3313522
33.3%
1 78488
 
0.8%

CANCELLATION_CODE
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing3235034
Missing (%)97.6%
Memory size50.6 MiB
B
39025 
A
33718 
C
5631 
D
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters78488
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 39025
 
1.2%
A 33718
 
1.0%
C 5631
 
0.2%
D 114
 
< 0.1%
(Missing) 3235034
97.6%

Length

2024-10-18T20:46:46.161453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T20:46:46.257473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
b 39025
49.7%
a 33718
43.0%
c 5631
 
7.2%
d 114
 
0.1%

Most occurring characters

ValueCountFrequency (%)
B 39025
49.7%
A 33718
43.0%
C 5631
 
7.2%
D 114
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 39025
49.7%
A 33718
43.0%
C 5631
 
7.2%
D 114
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 39025
49.7%
A 33718
43.0%
C 5631
 
7.2%
D 114
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 39025
49.7%
A 33718
43.0%
C 5631
 
7.2%
D 114
 
0.1%

DIVERTED
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
0.0
3306225 
1.0
 
7297

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9940566
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3306225
99.8%
1.0 7297
 
0.2%

Length

2024-10-18T20:46:46.359654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T20:46:46.449796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3306225
99.8%
1.0 7297
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 6619747
66.6%
. 3313522
33.3%
1 7297
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6619747
66.6%
. 3313522
33.3%
1 7297
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6619747
66.6%
. 3313522
33.3%
1 7297
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9940566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6619747
66.6%
. 3313522
33.3%
1 7297
 
0.1%

CRS_ELAPSED_TIME
Real number (ℝ)

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.81859
Minimum35
Maximum480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:46.551927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile65
Q185
median115
Q3155
95-th percentile240
Maximum480
Range445
Interquartile range (IQR)70

Descriptive statistics

Standard deviation56.677458
Coefficient of variation (CV)0.44342107
Kurtosis2.3429717
Mean127.81859
Median Absolute Deviation (MAD)35
Skewness1.3418643
Sum4.2352973 × 108
Variance3212.3343
MonotonicityNot monotonic
2024-10-18T20:46:46.678066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 205990
 
6.2%
90 186734
 
5.6%
80 177522
 
5.4%
70 164351
 
5.0%
75 159795
 
4.8%
95 121134
 
3.7%
135 117920
 
3.6%
65 116020
 
3.5%
110 115612
 
3.5%
140 112193
 
3.4%
Other values (74) 1836251
55.4%
ValueCountFrequency (%)
35 3861
 
0.1%
40 10156
 
0.3%
45 17613
 
0.5%
50 15092
 
0.5%
55 26486
 
0.8%
60 52045
 
1.6%
65 116020
3.5%
70 164351
5.0%
75 159795
4.8%
80 177522
5.4%
ValueCountFrequency (%)
480 1
 
< 0.1%
425 47
 
< 0.1%
420 34
 
< 0.1%
415 117
 
< 0.1%
410 144
 
< 0.1%
405 423
< 0.1%
400 372
 
< 0.1%
395 465
< 0.1%
390 683
< 0.1%
385 1012
< 0.1%

ELAPSED_TIME
Real number (ℝ)

Missing 

Distinct445
Distinct (%)< 0.1%
Missing85785
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean120.89473
Minimum22
Maximum524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:46.810205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile56
Q178
median109
Q3149
95-th percentile231
Maximum524
Range502
Interquartile range (IQR)71

Descriptive statistics

Standard deviation56.913093
Coefficient of variation (CV)0.47076572
Kurtosis2.3242189
Mean120.89473
Median Absolute Deviation (MAD)34
Skewness1.3345112
Sum3.9021638 × 108
Variance3239.1002
MonotonicityNot monotonic
2024-10-18T20:46:46.943272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 36240
 
1.1%
76 36078
 
1.1%
79 35957
 
1.1%
78 35937
 
1.1%
75 35770
 
1.1%
80 35119
 
1.1%
74 35094
 
1.1%
81 34326
 
1.0%
73 34318
 
1.0%
82 33606
 
1.0%
Other values (435) 2875292
86.8%
(Missing) 85785
 
2.6%
ValueCountFrequency (%)
22 1
 
< 0.1%
23 1
 
< 0.1%
25 6
 
< 0.1%
26 10
 
< 0.1%
27 53
 
< 0.1%
28 129
 
< 0.1%
29 266
 
< 0.1%
30 473
< 0.1%
31 782
< 0.1%
32 1074
< 0.1%
ValueCountFrequency (%)
524 1
< 0.1%
519 1
< 0.1%
494 1
< 0.1%
491 1
< 0.1%
488 2
< 0.1%
481 1
< 0.1%
477 1
< 0.1%
474 1
< 0.1%
473 1
< 0.1%
470 1
< 0.1%

AIR_TIME
Real number (ℝ)

Missing 

Distinct410
Distinct (%)< 0.1%
Missing85785
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean102.51606
Minimum12
Maximum503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:47.076196image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile40
Q160
median91
Q3130
95-th percentile210
Maximum503
Range491
Interquartile range (IQR)70

Descriptive statistics

Standard deviation55.640874
Coefficient of variation (CV)0.54275273
Kurtosis2.4592845
Mean102.51606
Median Absolute Deviation (MAD)33
Skewness1.3751223
Sum3.308949 × 108
Variance3095.9068
MonotonicityNot monotonic
2024-10-18T20:46:47.214033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 39494
 
1.2%
63 38843
 
1.2%
61 38754
 
1.2%
64 38208
 
1.2%
60 37843
 
1.1%
59 36680
 
1.1%
65 36485
 
1.1%
58 35868
 
1.1%
66 35478
 
1.1%
57 35184
 
1.1%
Other values (400) 2854900
86.2%
(Missing) 85785
 
2.6%
ValueCountFrequency (%)
12 1
 
< 0.1%
13 9
 
< 0.1%
14 84
 
< 0.1%
15 211
 
< 0.1%
16 534
 
< 0.1%
17 776
 
< 0.1%
18 943
 
< 0.1%
19 1418
 
< 0.1%
20 2331
0.1%
21 4163
0.1%
ValueCountFrequency (%)
503 1
 
< 0.1%
434 1
 
< 0.1%
433 1
 
< 0.1%
422 1
 
< 0.1%
419 3
< 0.1%
418 1
 
< 0.1%
416 1
 
< 0.1%
415 1
 
< 0.1%
413 2
< 0.1%
412 3
< 0.1%

DISTANCE
Real number (ℝ)

Distinct730
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean739.96997
Minimum73
Maximum2979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:47.346276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile223
Q1387
median641
Q3967
95-th percentile1620
Maximum2979
Range2906
Interquartile range (IQR)580

Descriptive statistics

Standard deviation464.34376
Coefficient of variation (CV)0.62751703
Kurtosis2.6530514
Mean739.96997
Median Absolute Deviation (MAD)279
Skewness1.4094771
Sum2.4519068 × 109
Variance215615.13
MonotonicityNot monotonic
2024-10-18T20:46:47.478928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 28881
 
0.9%
325 25119
 
0.8%
480 24667
 
0.7%
602 24529
 
0.7%
255 23348
 
0.7%
417 22670
 
0.7%
296 22470
 
0.7%
369 22019
 
0.7%
651 21531
 
0.6%
404 20374
 
0.6%
Other values (720) 3077914
92.9%
ValueCountFrequency (%)
73 6802
0.2%
84 2310
 
0.1%
100 15358
0.5%
102 9225
0.3%
133 1990
 
0.1%
137 4918
 
0.1%
141 3177
 
0.1%
148 8579
0.3%
153 3818
 
0.1%
159 7241
0.2%
ValueCountFrequency (%)
2979 232
 
< 0.1%
2917 1846
0.1%
2860 310
 
< 0.1%
2845 1592
< 0.1%
2818 1216
< 0.1%
2762 2568
0.1%
2717 1137
< 0.1%
2695 2273
0.1%
2676 486
 
< 0.1%
2615 1224
< 0.1%

DELAY_DUE_CARRIER
Real number (ℝ)

Missing  Zeros 

Distinct512
Distinct (%)0.1%
Missing2553684
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean17.178392
Minimum0
Maximum741
Zeros243307
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:47.605202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q320
95-th percentile68
Maximum741
Range741
Interquartile range (IQR)20

Descriptive statistics

Standard deviation31.1235
Coefficient of variation (CV)1.8117819
Kurtosis37.455361
Mean17.178392
Median Absolute Deviation (MAD)8
Skewness4.8687856
Sum13052795
Variance968.67227
MonotonicityNot monotonic
2024-10-18T20:46:47.742018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 243307
 
7.3%
6 20618
 
0.6%
7 20081
 
0.6%
8 18746
 
0.6%
4 18683
 
0.6%
3 18667
 
0.6%
2 18566
 
0.6%
5 18482
 
0.6%
9 17882
 
0.5%
1 17826
 
0.5%
Other values (502) 346980
 
10.5%
(Missing) 2553684
77.1%
ValueCountFrequency (%)
0 243307
7.3%
1 17826
 
0.5%
2 18566
 
0.6%
3 18667
 
0.6%
4 18683
 
0.6%
5 18482
 
0.6%
6 20618
 
0.6%
7 20081
 
0.6%
8 18746
 
0.6%
9 17882
 
0.5%
ValueCountFrequency (%)
741 1
< 0.1%
690 1
< 0.1%
667 1
< 0.1%
648 1
< 0.1%
612 1
< 0.1%
606 1
< 0.1%
588 1
< 0.1%
586 1
< 0.1%
577 1
< 0.1%
575 1
< 0.1%

DELAY_DUE_WEATHER
Real number (ℝ)

Missing  Zeros 

Distinct391
Distinct (%)0.1%
Missing2553684
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean1.1075506
Minimum0
Maximum726
Zeros744337
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:47.876224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum726
Range726
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.316744
Coefficient of variation (CV)10.217813
Kurtosis452.44609
Mean1.1075506
Median Absolute Deviation (MAD)0
Skewness17.654631
Sum841559
Variance128.0687
MonotonicityNot monotonic
2024-10-18T20:46:48.008268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 744337
 
22.5%
15 309
 
< 0.1%
17 305
 
< 0.1%
19 298
 
< 0.1%
16 295
 
< 0.1%
14 290
 
< 0.1%
20 290
 
< 0.1%
18 280
 
< 0.1%
12 278
 
< 0.1%
10 249
 
< 0.1%
Other values (381) 12907
 
0.4%
(Missing) 2553684
77.1%
ValueCountFrequency (%)
0 744337
22.5%
1 83
 
< 0.1%
2 122
 
< 0.1%
3 118
 
< 0.1%
4 147
 
< 0.1%
5 191
 
< 0.1%
6 223
 
< 0.1%
7 219
 
< 0.1%
8 218
 
< 0.1%
9 245
 
< 0.1%
ValueCountFrequency (%)
726 1
< 0.1%
702 1
< 0.1%
618 1
< 0.1%
581 1
< 0.1%
577 1
< 0.1%
571 1
< 0.1%
530 1
< 0.1%
526 1
< 0.1%
514 1
< 0.1%
510 1
< 0.1%

DELAY_DUE_NAS
Real number (ℝ)

Missing  Zeros 

Distinct402
Distinct (%)0.1%
Missing2553684
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean7.5263359
Minimum0
Maximum731
Zeros470725
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:48.146785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile35
Maximum731
Range731
Interquartile range (IQR)7

Descriptive statistics

Standard deviation19.26359
Coefficient of variation (CV)2.5594911
Kurtosis70.546487
Mean7.5263359
Median Absolute Deviation (MAD)0
Skewness6.4551743
Sum5718796
Variance371.08589
MonotonicityNot monotonic
2024-10-18T20:46:48.278395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 470725
 
14.2%
1 18657
 
0.6%
2 17245
 
0.5%
3 15357
 
0.5%
4 14252
 
0.4%
5 13201
 
0.4%
6 11908
 
0.4%
7 11020
 
0.3%
15 10216
 
0.3%
8 10149
 
0.3%
Other values (392) 167108
 
5.0%
(Missing) 2553684
77.1%
ValueCountFrequency (%)
0 470725
14.2%
1 18657
 
0.6%
2 17245
 
0.5%
3 15357
 
0.5%
4 14252
 
0.4%
5 13201
 
0.4%
6 11908
 
0.4%
7 11020
 
0.3%
8 10149
 
0.3%
9 9281
 
0.3%
ValueCountFrequency (%)
731 1
< 0.1%
586 1
< 0.1%
522 1
< 0.1%
517 1
< 0.1%
515 1
< 0.1%
500 1
< 0.1%
486 1
< 0.1%
485 1
< 0.1%
479 1
< 0.1%
477 1
< 0.1%

DELAY_DUE_SECURITY
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct197
Distinct (%)< 0.1%
Missing2553684
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean0.1449151
Minimum0
Maximum581
Zeros756232
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:48.411862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum581
Range581
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.284715
Coefficient of variation (CV)22.666478
Kurtosis4098.7235
Mean0.1449151
Median Absolute Deviation (MAD)0
Skewness49.641986
Sum110112
Variance10.789352
MonotonicityNot monotonic
2024-10-18T20:46:48.550604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 756232
 
22.8%
16 176
 
< 0.1%
15 170
 
< 0.1%
17 139
 
< 0.1%
18 133
 
< 0.1%
19 113
 
< 0.1%
20 113
 
< 0.1%
12 112
 
< 0.1%
14 103
 
< 0.1%
21 103
 
< 0.1%
Other values (187) 2444
 
0.1%
(Missing) 2553684
77.1%
ValueCountFrequency (%)
0 756232
22.8%
1 36
 
< 0.1%
2 45
 
< 0.1%
3 49
 
< 0.1%
4 36
 
< 0.1%
5 58
 
< 0.1%
6 79
 
< 0.1%
7 95
 
< 0.1%
8 80
 
< 0.1%
9 88
 
< 0.1%
ValueCountFrequency (%)
581 1
< 0.1%
454 1
< 0.1%
415 1
< 0.1%
332 1
< 0.1%
308 1
< 0.1%
305 1
< 0.1%
300 1
< 0.1%
290 1
< 0.1%
278 1
< 0.1%
246 1
< 0.1%

DELAY_DUE_LATE_AIRCRAFT
Real number (ℝ)

Missing  Zeros 

Distinct461
Distinct (%)0.1%
Missing2553684
Missing (%)77.1%
Infinite0
Infinite (%)0.0%
Mean25.346619
Minimum0
Maximum636
Zeros232743
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size50.6 MiB
2024-10-18T20:46:48.682745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q333
95-th percentile98
Maximum636
Range636
Interquartile range (IQR)33

Descriptive statistics

Standard deviation37.815797
Coefficient of variation (CV)1.4919464
Kurtosis14.563172
Mean25.346619
Median Absolute Deviation (MAD)14
Skewness3.1338536
Sum19259324
Variance1430.0345
MonotonicityNot monotonic
2024-10-18T20:46:48.814899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 232743
 
7.0%
15 15020
 
0.5%
16 14359
 
0.4%
17 13616
 
0.4%
18 12878
 
0.4%
14 12289
 
0.4%
19 12063
 
0.4%
13 12003
 
0.4%
12 11694
 
0.4%
11 11603
 
0.4%
Other values (451) 411570
 
12.4%
(Missing) 2553684
77.1%
ValueCountFrequency (%)
0 232743
7.0%
1 10957
 
0.3%
2 11014
 
0.3%
3 10134
 
0.3%
4 9637
 
0.3%
5 9937
 
0.3%
6 10669
 
0.3%
7 10475
 
0.3%
8 10892
 
0.3%
9 11049
 
0.3%
ValueCountFrequency (%)
636 1
< 0.1%
615 1
< 0.1%
606 1
< 0.1%
558 1
< 0.1%
556 1
< 0.1%
551 1
< 0.1%
531 1
< 0.1%
511 1
< 0.1%
507 1
< 0.1%
504 1
< 0.1%

YEAR
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 MiB
2022
1307149 
2021
1064640 
2023
941733 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters13254088
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2022 1307149
39.4%
2021 1064640
32.1%
2023 941733
28.4%

Length

2024-10-18T20:46:48.935011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T20:46:49.031120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2022 1307149
39.4%
2021 1064640
32.1%
2023 941733
28.4%

Most occurring characters

ValueCountFrequency (%)
2 7934193
59.9%
0 3313522
25.0%
1 1064640
 
8.0%
3 941733
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13254088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 7934193
59.9%
0 3313522
25.0%
1 1064640
 
8.0%
3 941733
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13254088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 7934193
59.9%
0 3313522
25.0%
1 1064640
 
8.0%
3 941733
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13254088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 7934193
59.9%
0 3313522
25.0%
1 1064640
 
8.0%
3 941733
 
7.1%

MONTH
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2173159
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.9 MiB
2024-10-18T20:46:49.121203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2422985
Coefficient of variation (CV)0.52149489
Kurtosis-1.011994
Mean6.2173159
Median Absolute Deviation (MAD)3
Skewness0.10514414
Sum20601213
Variance10.5125
MonotonicityNot monotonic
2024-10-18T20:46:49.223745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 349077
10.5%
7 348269
10.5%
6 328785
9.9%
5 317834
9.6%
3 302291
9.1%
4 299130
9.0%
1 271173
8.2%
2 245153
7.4%
10 219820
6.6%
12 213568
6.4%
Other values (2) 418422
12.6%
ValueCountFrequency (%)
1 271173
8.2%
2 245153
7.4%
3 302291
9.1%
4 299130
9.0%
5 317834
9.6%
6 328785
9.9%
7 348269
10.5%
8 349077
10.5%
9 209848
6.3%
10 219820
6.6%
ValueCountFrequency (%)
12 213568
6.4%
11 208574
6.3%
10 219820
6.6%
9 209848
6.3%
8 349077
10.5%
7 348269
10.5%
6 328785
9.9%
5 317834
9.6%
4 299130
9.0%
3 302291
9.1%

Interactions

2024-10-18T20:46:13.961732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:11.481266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:17.222832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:22.495222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:27.828950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:33.383139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:38.580121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:43.800910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:49.110418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:54.567079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:59.548689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:02.369464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:05.154392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:07.948909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:10.926850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:14.397806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:11.934452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:17.637826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:22.914001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:28.285592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:33.827193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:39.024225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:44.255861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:49.561660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:55.021180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:59.742229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:02.563584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:05.346293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:08.139134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:11.124239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:14.823934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:12.388476image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:18.055838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:23.334059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:28.728357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:34.258642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:39.466624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:44.694488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:49.994930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:55.466085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:59.935818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:02.751839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:05.536605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:08.326275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:11.321333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:15.261161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:12.854888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:18.495724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:23.783034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:29.158887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:34.702144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:39.916831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:45.149883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:50.449588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:55.922258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:00.137011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:02.949002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:05.731060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:08.518347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:11.518571image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:15.690952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:13.313436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:18.934985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:24.222775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:29.593450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:35.112895image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:40.353172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:45.583402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:50.888926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:56.356114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:00.324427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:03.130452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:05.918744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:08.709400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:11.703477image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:16.079651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:13.720127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:19.349537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:24.673836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:30.041782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:35.527427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:40.743457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:46.016955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:51.312791image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:56.761521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:00.515173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:03.318824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:06.109284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:08.896804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:11.896453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:16.500652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:14.174051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:19.783190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:25.104218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:30.482275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:35.946091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:41.162772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:46.431767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:51.746409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:57.189253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:00.709135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:03.508392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:06.297446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:09.082178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:12.084626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:16.922770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:14.624799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:20.210394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:25.537614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:30.922425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:36.368048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:41.596549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:46.857392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:52.158369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:57.616773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:00.887806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:03.684505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:06.481429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:09.257588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:12.275746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:17.310416image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:15.055252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:20.638241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:25.978915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:31.366849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:36.788654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:42.001087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:47.289809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:52.600492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:58.010264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:01.069733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:03.869227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:06.657603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:09.436505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:12.454680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:17.497143image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:15.590847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:20.824505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:26.176212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:31.557790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:36.973076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:42.181545image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:47.482246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:52.782523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:58.197167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:01.248923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:04.051579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:06.844789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:09.616546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:12.648402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:17.681567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:15.786011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:21.021419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:26.370614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:31.763717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:37.163733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:42.377371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:47.669371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:52.969762image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:58.391182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:01.431853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:04.224359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:07.030551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:09.802569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:12.831038image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:17.866783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:15.984527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:21.221201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:26.566288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:31.957700image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:37.354708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:42.564739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:47.852134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:53.163972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:58.575940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:01.624387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:04.409767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:07.209111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:09.984527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:13.019021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:18.051500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:16.179006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:21.416801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:26.759845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:32.299205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:37.542404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:42.764303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:48.042087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:53.348699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:58.769858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:01.815402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:04.599680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:07.391260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:10.157238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:13.213029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:18.236818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:16.376392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:21.612152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:26.951202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:32.497083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:37.730371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:42.954702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:48.228947image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:53.533499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:58.951298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:01.997048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:04.781569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:07.576057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:10.557448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:13.384409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:18.608754image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:16.780223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:22.054511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:27.382359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:32.944471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:38.176025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:43.359026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:48.671200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:54.166286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:45:59.361589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:02.190495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:04.967804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:07.763380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:10.748373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-10-18T20:46:13.576858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-10-18T20:46:19.828241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-18T20:46:24.287908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-18T20:46:34.699751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FL_DATEAIRLINEAIRLINE_DOTAIRLINE_CODEDOT_CODEFL_NUMBERORIGINORIGIN_CITYDESTDEST_CITYCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTWHEELS_OFFWHEELS_ONTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDCRS_ELAPSED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEDELAY_DUE_CARRIERDELAY_DUE_WEATHERDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_LATE_AIRCRAFTYEARMONTH
1062021-01-01Southwest Airlines Co.Southwest Airlines Co.: WNWN193932527BWIBaltimore, MDBNANashville, TN12:5512:53-2.09.013:0213:416.013:5513:47-8.00.0NaN0.0120.0114.099.0587.0NaNNaNNaNNaNNaN20211
13642021-01-01Southwest Airlines Co.Southwest Airlines Co.: WNWN193931782ATLAtlanta, GAMCIKansas City, MO16:3516:416.012.016:5317:399.017:5017:48-2.00.0NaN0.0135.0127.0106.0692.0NaNNaNNaNNaNNaN20211
13652021-01-01Southwest Airlines Co.Southwest Airlines Co.: WNWN193932003ATLAtlanta, GABWIBaltimore, MD12:0011:58-2.08.012:0613:2110.013:4513:31-14.00.0NaN0.0105.093.075.0577.0NaNNaNNaNNaNNaN20211
13662021-01-01Southwest Airlines Co.Southwest Airlines Co.: WNWN193932823ATLAtlanta, GABWIBaltimore, MD17:2517:294.013.017:4219:0424.019:0519:2823.00.0NaN0.0100.0119.082.0577.00.00.023.00.00.020211
13672021-01-01Southwest Airlines Co.Southwest Airlines Co.: WNWN193932707ATLAtlanta, GALGANew York, NY19:5020:2232.014.020:3622:077.022:0522:149.00.0NaN0.0135.0112.091.0762.0NaNNaNNaNNaNNaN20211
13682021-01-01Southwest Airlines Co.Southwest Airlines Co.: WNWN193932568ATLAtlanta, GALASLas Vegas, NV16:3016:4717.012.016:5917:545.018:0517:59-6.00.0NaN0.0275.0252.0235.01747.0NaNNaNNaNNaNNaN20211
13692021-01-01Southwest Airlines Co.Southwest Airlines Co.: WNWN193933796ATLAtlanta, GAJAXJacksonville, FL12:4512:5611.015.013:1113:594.013:5514:038.00.0NaN0.070.067.048.0270.0NaNNaNNaNNaNNaN20211
13702021-01-01Southwest Airlines Co.Southwest Airlines Co.: WNWN193933179ATLAtlanta, GAINDIndianapolis, IN12:4012:39-1.027.013:0614:162.014:1014:188.00.0NaN0.090.099.070.0432.0NaNNaNNaNNaNNaN20211
13712021-01-01Southwest Airlines Co.Southwest Airlines Co.: WNWN193932434ATLAtlanta, GAINDIndianapolis, IN17:4018:0424.016.018:2019:236.019:1019:2919.00.0NaN0.090.085.063.0432.07.00.00.00.012.020211
13722021-01-01Southwest Airlines Co.Southwest Airlines Co.: WNWN193932970ATLAtlanta, GAIADWashington, DC12:4013:0727.014.013:2114:432.014:2014:4525.00.0NaN0.0100.098.082.0534.025.00.00.00.00.020211
FL_DATEAIRLINEAIRLINE_DOTAIRLINE_CODEDOT_CODEFL_NUMBERORIGINORIGIN_CITYDESTDEST_CITYCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTWHEELS_OFFWHEELS_ONTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDCRS_ELAPSED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEDELAY_DUE_CARRIERDELAY_DUE_WEATHERDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_LATE_AIRCRAFTYEARMONTH
172699262023-08-31Southwest Airlines Co.Southwest Airlines Co.: WNWN193931378DCAWashington, DCMDWChicago, IL18:3018:311.09.018:4019:098.019:3019:17-13.00.0NaN0.0120.0106.089.0601.0NaNNaNNaNNaNNaN20238
172699292023-08-31Southwest Airlines Co.Southwest Airlines Co.: WNWN19393747DCAWashington, DCMDWChicago, IL09:1509:12-3.029.009:4110:093.010:1010:122.00.0NaN0.0115.0120.088.0601.0NaNNaNNaNNaNNaN20238
172699312023-08-31Southwest Airlines Co.Southwest Airlines Co.: WNWN19393713DCAWashington, DCMDWChicago, IL11:5011:47-3.07.011:5412:233.012:4512:26-19.00.0NaN0.0115.099.089.0601.0NaNNaNNaNNaNNaN20238
172699342023-08-31Southwest Airlines Co.Southwest Airlines Co.: WNWN193933710DCAWashington, DCMCOOrlando, FL06:0506:061.08.006:1408:014.008:2008:05-15.00.0NaN0.0135.0119.0107.0759.0NaNNaNNaNNaNNaN20238
172699352023-08-31Southwest Airlines Co.Southwest Airlines Co.: WNWN193933664DCAWashington, DCMCIKansas City, MO12:5012:44-6.012.012:5614:034.014:2514:07-18.00.0NaN0.0155.0143.0127.0949.0NaNNaNNaNNaNNaN20238
172699372023-08-31Southwest Airlines Co.Southwest Airlines Co.: WNWN193933106DCAWashington, DCMCIKansas City, MO16:2516:24-1.07.016:3117:383.018:0017:41-19.00.0NaN0.0155.0137.0127.0949.0NaNNaNNaNNaNNaN20238
172699392023-08-31Southwest Airlines Co.Southwest Airlines Co.: WNWN193933068DALDallas, TXSATSan Antonio, TX21:5022:0515.07.022:1222:523.022:5022:555.00.0NaN0.060.050.040.0247.0NaNNaNNaNNaNNaN20238
172699402023-08-31Southwest Airlines Co.Southwest Airlines Co.: WNWN193934795DALDallas, TXLAXLos Angeles, CA14:5015:1424.015.015:2916:1510.016:0016:2525.00.0NaN0.0190.0191.0166.01246.04.00.01.00.020.020238
172699412023-08-31Southwest Airlines Co.Southwest Airlines Co.: WNWN193935171DALDallas, TXMCOOrlando, FL20:4520:472.08.020:5500:076.000:2000:13-7.00.0NaN0.0155.0146.0132.0973.0NaNNaNNaNNaNNaN20238
172699422023-08-31Southwest Airlines Co.Southwest Airlines Co.: WNWN193931902DCAWashington, DCMCOOrlando, FL14:0014:000.012.014:1216:507.016:2016:5737.00.0NaN0.0140.0177.0158.0759.00.00.037.00.00.020238